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Adaptive edge-based side-match finite-state classified vector quantization with quadtree map.

R F Chang1, W M Chen

  • 1Dept. of Comput. Sci. and Inf. Eng., Nat. Chung Cheng Univ., Chiayi.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1996
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive edge-based classified vector quantizer (FSVQ) for improved image coding. The new method enhances image quality by reducing block boundary artifacts, outperforming standard techniques.

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Area of Science:

  • Digital image processing
  • Image compression
  • Signal processing

Background:

  • Vector quantization (VQ) is effective for low bit-rate image coding.
  • Side-match finite-state vector quantization (SMVQ) leverages inter-block correlations to minimize gray level transitions.
  • Existing methods face challenges in accurately classifying and coding edge blocks.

Purpose of the Study:

  • To propose a novel adaptive edge-based side-match finite-state classified vector quantizer (classified FSVQ) using a quadtree map.
  • To enhance image coding performance by improving the classification of edge blocks.
  • To reduce visible block boundaries and edge degradation in compressed images.

Main Methods:

  • Developed a classified FSVQ that categorizes blocks into edge and non-edge types to prevent incorrect codebook selection.
  • Implemented a quadtree map for adaptive classification.
  • Reclassified edge vectors into 16 distinct classes, each with a unique master codebook.

Main Results:

  • The classified FSVQ achieved up to 1.16 dB improvement over ordinary SMVQ at similar bit rates.
  • Compared to ordinary VQ, the classified FSVQ showed an improvement of up to 2.08 dB for the Lena image at the same bit rate.
  • Visible block boundaries and edge degradation were significantly reduced due to edge-vector classification.

Conclusions:

  • The proposed classified FSVQ offers superior image compression performance compared to ordinary VQ and SMVQ.
  • Edge-vector classification in FSVQ effectively minimizes artifacts, leading to better perceptual image quality.
  • The adaptive edge-based approach represents a significant advancement in finite-state vector quantization for image coding.